arXiv subjects

Oktie Hassanzadeh

Publications and source records attributed to Oktie Hassanzadeh.

21 records · Page 2Linked to original sources

arXiv2009

LinkedCT: A Linked Data Space for Clinical Trials

The Linked Clinical Trials (LinkedCT) project aims at publishing the first open semantic web data source for clinical trials data. The database exposed by LinkedCT is generated by (1) transforming existing data sources of clinical trials into RDF, and (2) discovering semantic links between the records in the trials data and several other data sources. In this paper, we discuss several challenges involved in these two steps and present the methodology used in LinkedCT to overcome these challenges. Our approach for semantic link discovery involves using state-of-the-art approximate string matching techniques combined with ontology-based semantic matching of the records, all performed in a declarative and easy-to-use framework. We present an evaluation of the performance of our proposed techniques in several link discovery scenarios in LinkedCT.

cs.DB↗

arXiv2009

Benchmarking Declarative Approximate Selection Predicates

Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize data quality primitives on top of any relational data source. A primary advantage of such an approach is the ease of use and integration with existing applications. Several similarity predicates have been proposed in the past for common quality primitives (approximate selections, joins, etc.) and have been fully expressed using declarative SQL statements. In this thesis, new similarity predicates are proposed along with their declarative realization, based on notions of probabilistic information retrieval. Then, full declarative specifications of previously proposed similarity predicates in the literature are presented, grouped into classes according to their primary characteristics. Finally, a thorough performance and accuracy study comparing a large number of similarity predicates for data cleaning operations is performed.

cs.DB↗

arXiv2009

Automated Protein Structure Classification: A Survey

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.

cs.CE↗